/* Copyright 2025 The JAX Authors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "jaxlib/py_socket_transfer.h"

#include <cstddef>
#include <cstdint>
#include <memory>
#include <optional>
#include <stdexcept>
#include <string>
#include <tuple>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/str_format.h"
#include "absl/strings/str_join.h"
#include "absl/time/time.h"
#include "llvm/Support/Casting.h"
#include "nanobind/nanobind.h"
#include "nanobind/stl/array.h"  // IWYU pragma: keep
#include "nanobind/stl/shared_ptr.h"  // IWYU pragma: keep
#include "nanobind/stl/string.h"  // IWYU pragma: keep
#include "nanobind/stl/string_view.h"  // IWYU pragma: keep
#include "nanobind/stl/vector.h"  // IWYU pragma: keep
#include "jaxlib/nb_class_ptr.h"
#include "jaxlib/py_array.h"
#include "jaxlib/py_client.h"
#include "jaxlib/py_executable.h"
#include "jaxlib/py_user_context.h"
#include "jaxlib/to_ifrt_sharding.h"
#include "xla/future.h"
#include "xla/pjrt/distributed/client.h"
#include "xla/pjrt/distributed/key_value_store_interface.h"
#include "xla/pjrt/pjrt_client.h"
#include "xla/pjrt/status_casters.h"
#include "xla/python/ifrt/array.h"
#include "xla/python/ifrt/array_spec.h"
#include "xla/python/ifrt/dtype.h"
#include "xla/python/ifrt/memory.h"
#include "xla/python/ifrt/shape.h"
#include "xla/python/ifrt/sharding.h"
#include "xla/python/nb_numpy.h"
#include "xla/python/pjrt_ifrt/pjrt_array.h"
#include "xla/python/pjrt_ifrt/pjrt_device.h"
#include "xla/python/pjrt_ifrt/pjrt_dtype.h"
#include "xla/python/pjrt_ifrt/pjrt_memory.h"
#include "xla/python/pjrt_ifrt/transfer_server_interface.h"
#include "xla/python/transfer/event_loop.h"
#include "xla/python/transfer/pjrt_transfer_server.h"
#include "xla/python/transfer/socket-server.h"
#include "xla/python/transfer/socket_bulk_transport.h"
#include "xla/python/transfer/streaming.h"
#include "xla/python/transfer/streaming_ifrt.h"
#include "xla/python/transfer/transfer_socket.pb.h"
#include "xla/python/types.h"
#include "xla/python/version.h"
#include "xla/tsl/concurrency/ref_count.h"
#include "xla/tsl/platform/statusor.h"
#include "xla/util.h"
#include "tsl/platform/casts.h"

namespace aux {

namespace nb = nanobind;

absl::StatusOr<xla::PjRtMemorySpace*> MemorySpaceFromSharding(
    const xla::ifrt::Sharding& sharding) {
  if (sharding.devices()->devices().size() != 1) {
    return xla::InvalidArgument(
        "Can only convert SingleDeviceSharding to MemorySpace not %s",
        sharding.DebugString());
  }
  auto* device = sharding.devices()->devices()[0];
  if (sharding.memory_kind().memory_kind().has_value()) {
    // Find `PjRtMemorySpace` that is associated with the sharding's device
    // and matches the sharding's memory_kind.
    xla::ifrt::Memory* memory = nullptr;
    for (xla::ifrt::Memory* ms : device->Memories()) {
      if (ms->Kind() == sharding.memory_kind()) {
        memory = ms;
        break;
      }
    }
    if (memory == nullptr) {
      return xla::InvalidArgument(
          "Invalid memory kind: %s; available memory kinds: %s",
          *sharding.memory_kind().memory_kind(),
          absl::StrJoin(sharding.devices()->devices().front()->Memories(), ", ",
                        [](std::string* out, xla::ifrt::Memory* ms) {
                          absl::StrAppend(out, *ms->Kind().memory_kind());
                        }));
    }
    return tensorflow::down_cast<xla::ifrt::PjRtMemory*>(memory)->pjrt_memory();
  } else {
    if (!device->IsAddressable()) {
      return xla::InvalidArgument(
          "Cannot copy array to non-addressable device %s",
          device->DebugString());
    }
    return tensorflow::down_cast<xla::ifrt::PjRtDevice*>(device)
        ->pjrt_device()
        ->default_memory_space();
  }
}

absl::StatusOr<tsl::RCReference<PullTable::Entry>> CreatePullEntry(
    const std::vector<xla::ifrt::ArrayRef>& arrs,
    std::shared_ptr<PremappedCopierState> state, size_t xfer_size,
    bool use_raw_buffers) {
  if (use_raw_buffers) {
    std::vector<RawBufferEntry::BufferRef> refs;
    for (auto& arr : arrs) {
      auto* pjrt_arr = llvm::dyn_cast_or_null<xla::ifrt::PjRtArray>(arr.get());
      if (pjrt_arr == nullptr) {
        return absl::InvalidArgumentError(
            "Cannot remote transfer non-pjrt arrays.");
      }
      for (auto& pjrt_buf : pjrt_arr->pjrt_buffers()) {
        TF_ASSIGN_OR_RETURN(size_t buf_size,
                            pjrt_buf->GetOnDeviceSizeInBytes());
        TF_ASSIGN_OR_RETURN(
            auto raw_buffer,
            xla::PjRtRawBuffer::CreateRawAliasOfBuffer(pjrt_buf.get()));
        refs.push_back(
            {pjrt_buf->GetReadyFuture(), std::move(raw_buffer), buf_size});
      }
    }
    return tsl::MakeRef<RawBufferEntry>(std::move(refs), state, xfer_size);
  }

  std::vector<PjRtBufferEntry::BufferRef> refs;
  for (auto& arr : arrs) {
    auto* pjrt_arr = llvm::dyn_cast_or_null<xla::ifrt::PjRtArray>(arr.get());
    if (pjrt_arr == nullptr) {
      return absl::InvalidArgumentError(
          "Cannot remote transfer non-pjrt arrays.");
    }
    for (auto& pjrt_buf : pjrt_arr->pjrt_buffers()) {
      TF_ASSIGN_OR_RETURN(size_t buf_size, pjrt_buf->GetOnDeviceSizeInBytes());
      refs.push_back({pjrt_buf, buf_size});
    }
  }
  return tsl::MakeRef<PjRtBufferEntry>(std::move(refs), state, xfer_size);
}

class PyTransferServerConnection {
 public:
  explicit PyTransferServerConnection(
      tsl::RCReference<SocketServer::Connection> conn)
      : conn_(std::move(conn)) {}

  void Pull(uint64_t uuid, std::vector<int> buffer_ids,
            std::vector<tsl::RCReference<ChunkDestination>> pull_dests) {
    if (buffer_ids.size() < 64) {
      conn_->Pull(uuid, buffer_ids, std::move(pull_dests));
    } else {
      for (size_t i = 0; i < buffer_ids.size(); ++i) {
        conn_->Pull(uuid, buffer_ids[i], std::move(pull_dests[i]));
      }
    }
  }

  SocketServer::Connection& conn() { return *conn_; }

 private:
  tsl::RCReference<SocketServer::Connection> conn_;
};

class PyTransferServer {
 public:
  PyTransferServer() = default;
  absl::Status Start(xla::ifrt::Client* client, size_t max_num_parallel_copies,
                     size_t xfer_size, const SocketAddress& addr,
                     const std::vector<SocketAddress>& transport_addresses,
                     bool supports_pinned_allocator, bool use_raw_buffers) {
    use_raw_buffers_ = use_raw_buffers;
    std::shared_ptr<BulkTransportFactory> factory;
    std::shared_ptr<xla::PjRtClient> pjrt_client =
        tensorflow::down_cast<xla::ifrt::PjRtClient*>(client)
            ->shared_ptr_pjrt_client();
    if (transport_addresses.empty()) {
      factory = BulkTransportFactory::CreateLocal();
    } else {
      auto tmp = xla::ValueOrThrow(
          AllocateAlignedMemory(xfer_size * max_num_parallel_copies));
      SlabAllocator uallocator(xla::ValueOrThrow(MapPjrtMemory(
                                   pjrt_client, tmp->data(), tmp->size(), tmp)),
                               xfer_size);
      std::optional<SlabAllocator> pinned_allocator;
      if (supports_pinned_allocator) {
        auto tmp = xla::ValueOrThrow(
            AllocateNetworkPinnedMemory(xfer_size * max_num_parallel_copies));
        pinned_allocator.emplace(
            xla::ValueOrThrow(
                MapPjrtMemory(pjrt_client, tmp->data(), tmp->size(), tmp)),
            xfer_size);
      }
      factory = xla::ValueOrThrow(CreateSocketBulkTransportFactory(
          transport_addresses, pinned_allocator, uallocator));
    }

    server_ = std::make_shared<SocketServer>();

    TF_ASSIGN_OR_RETURN(
        auto mem, AllocateAndMapPjrtMemory(
                      pjrt_client, max_num_parallel_copies * xfer_size * 2));
    premapped_copier_ = std::make_shared<PremappedCopierState>(
        mem, max_num_parallel_copies, xfer_size);
    xfer_size_ = xfer_size;
    return server_->Start(addr, factory);
  }
  std::string address() { return server_->addr().ToString(); }

  PyTransferServerConnection Connect(const std::string& saddr) {
    return PyTransferServerConnection(
        server_->Connect(xla::ValueOrThrow(SocketAddress::Parse(saddr))));
  }

  void AwaitPull(uint64_t uuid, const std::vector<xla::ifrt::ArrayRef>& arrs) {
    server_->AwaitPull(
        uuid, xla::ValueOrThrow(CreatePullEntry(arrs, premapped_copier_,
                                                xfer_size_, use_raw_buffers_)));
  }

  void Reset() { server_->Reset(); }

  size_t xfer_size() { return xfer_size_; }

  std::shared_ptr<PremappedCopierState> premapped_copier() {
    return premapped_copier_;
  }

 private:
  std::shared_ptr<SocketServer> server_;
  std::shared_ptr<PremappedCopierState> premapped_copier_;
  size_t xfer_size_;
  bool use_raw_buffers_ = false;
};

absl::StatusOr<xla::ifrt::ArraySpec> ArraySpecFromShapeDtypeStruct(
    nb::handle aval) {
  TF_ASSIGN_OR_RETURN(xla::ifrt::DType dtype,
                      xla::DtypeToIfRtDType(
                          nb::borrow<xla::nb_dtype>(aval.attr("dtype").ptr())));
  auto shape_dims = nb::cast<std::vector<int64_t>>(aval.attr("shape"));
  auto shape = xla::ifrt::Shape(
      xla::ifrt::Shape::Dimensions(shape_dims.begin(), shape_dims.end()));
  TF_ASSIGN_OR_RETURN(auto sharding,
                      jax::GetIfrtHloSharding(aval.attr("sharding"), shape));
  return xla::ifrt::ArraySpec{dtype, std::move(shape), std::move(sharding)};
}

struct BufferSource {
  xla::ifrt::ArrayRef arr;
  xla::PjRtBuffer* buffer;
};

struct CopyDests {
  std::vector<xla::PjRtClient::ShapeSpec> shape_specs;
  xla::PjRtMemorySpace* memory_space;
};

void RegisterTransferServerTypes(nanobind::module_& m) {
  nb::class_<PyTransferServerConnection>(m, "TransferConnection")
      .def(
          "_testonly_inject_failure",
          [](PyTransferServerConnection& self) { self.conn().InjectFailure(); })
      .def("_poison_connection",
           [](PyTransferServerConnection& self) {
             self.conn().InjectFailure(aux::SocketServer::Connection::kPoison);
           })
      .def("_pull_flat",
           [](PyTransferServerConnection& self, nb::int_ uuid,
              jax::nb_class_ptr<jax::PyClient> py_client,
              std::vector<nb::object> py_avals) {
             auto* ifrt_client = llvm::dyn_cast_or_null<xla::ifrt::PjRtClient>(
                 py_client->ifrt_client());
             if (ifrt_client == nullptr) {
               xla::ThrowIfError(absl::InvalidArgumentError(
                   "_pull_flat only supported on pjrt-ifrt clients."));
             }

             jax::PyUserContextScope user_context_scope;
             std::vector<xla::ifrt::ArraySpec> avals;
             std::vector<nb::object> shardings;
             shardings.reserve(py_avals.size());
             avals.reserve(py_avals.size());
             for (const auto& py_aval : py_avals) {
               avals.push_back(
                   xla::ValueOrThrow(ArraySpecFromShapeDtypeStruct(py_aval)));
               shardings.push_back(py_aval.attr("sharding"));
             }

             std::vector<CopyDests> dests;
             std::vector<std::pair<int, int>> fetch_idxs;
             absl::flat_hash_map<xla::PjRtMemorySpace*, int> mapping;
             std::vector<std::vector<std::pair<int, int>>> buffer_list;

             for (auto& aval : avals) {
               std::vector<std::pair<int, int>> buf_list;
               auto prim_type =
                   xla::ValueOrThrow(xla::ifrt::ToPrimitiveType(aval.dtype));
               auto shards = xla::ValueOrThrow(aval.sharding->Disassemble(
                   aval.shape,
                   xla::ifrt::SingleDeviceShardSemantics::kAddressableShards));
               buf_list.reserve(shards.size());
               for (auto& shard : shards) {
                 auto* mem_space =
                     xla::ValueOrThrow(MemorySpaceFromSharding(*shard.second));
                 int dest_idx =
                     mapping.emplace(mem_space, static_cast<int>(dests.size()))
                         .first->second;
                 if (dest_idx == dests.size()) {
                   dests.emplace_back();
                   dests.back().memory_space = mem_space;
                 }
                 fetch_idxs.push_back(
                     {dest_idx,
                      static_cast<int>(dests[dest_idx].shape_specs.size())});
                 buf_list.push_back(fetch_idxs.back());
                 dests[dest_idx].shape_specs.push_back(
                     {prim_type,
                      xla::DimensionVector(shard.first.dims().begin(),
                                           shard.first.dims().end())});
               }
               buffer_list.push_back(std::move(buf_list));
             }

             std::vector<std::shared_ptr<
                 xla::PjRtClient::AsyncHostToDeviceTransferManager>>
                 atms;
             atms.reserve(dests.size());

             for (auto& dest : dests) {
               atms.push_back(xla::ValueOrThrow(
                   py_client->pjrt_client()->CreateBuffersForAsyncHostToDevice(
                       dest.shape_specs, std::nullopt, dest.memory_space)));
             }

             std::vector<tsl::RCReference<ChunkDestination>> pull_dests;
             std::vector<int> buffer_ids;
             pull_dests.reserve(fetch_idxs.size());
             buffer_ids.reserve(fetch_idxs.size());
             for (auto& fetch_idx : fetch_idxs) {
               auto& atm = atms[fetch_idx.first];
               pull_dests.push_back(MakeDmaDestination(
                   atm, fetch_idx.second, atm->buffer_size(fetch_idx.second)));
               buffer_ids.push_back(static_cast<int>(buffer_ids.size()));
             }

             uint64_t uuid_cpp;
             try {
               uuid_cpp = static_cast<uint64_t>(uuid);
             } catch (std::out_of_range& e) {
               throw nb::value_error(
                   "_pull_flat requires uuid to fit in a uint64_t");
             }
             self.Pull(uuid_cpp, buffer_ids, std::move(pull_dests));

             std::vector<jax::PyArray> out;
             for (size_t i = 0; i < buffer_list.size(); ++i) {
               xla::ifrt::PjRtArray::PjRtBuffers buffers;
               buffers.reserve(buffer_list[i].size());
               for (auto& v : buffer_list[i]) {
                 buffers.push_back(atms[v.first]->RetrieveBuffer(v.second));
               }
               auto arr = xla::ValueOrThrow(xla::ifrt::PjRtArray::Create(
                   ifrt_client, avals[i].dtype, avals[i].shape,
                   avals[i].sharding, std::move(buffers), avals[i].layout));
               out.push_back(jax::PyArray::MakeFromIfrtArrayAndSharding(
                   py_client, std::move(arr), shardings[i], false, true,
                   /*skip_checks=*/false));
             }

             return out;
           })
      .def("_pull_into_flat", [](PyTransferServerConnection& self,
                                 nb::int_ uuid, std::vector<jax::PyArray> dests,
                                 std::vector<nb::slice> slices_per_array) {
        if (dests.size() != slices_per_array.size()) {
          throw nb::value_error(
              absl::StrFormat("Expected dests and slices to have the same "
                              "size, got: %d vs %d",
                              dests.size(), slices_per_array.size())
                  .c_str());
        }
        std::vector<tsl::RCReference<xla::ifrt::PjRtCompatibleArray>> arrs;
        arrs.reserve(dests.size());
        for (const jax::PyArray& dest : dests) {
          arrs.push_back(tsl::FormRef(
              tensorflow::down_cast<xla::ifrt::PjRtCompatibleArray*>(
                  dest.ifrt_array())));
        }
        uint64_t uuid_cpp;
        try {
          uuid_cpp = static_cast<uint64_t>(uuid);
        } catch (std::out_of_range& e) {
          throw nb::value_error(
              "_await_pull_flat requires uuid to fit in a uint64_t");
        }
        size_t i = 0;
        std::vector<jax::PyToken> futures;
        std::vector<tsl::RCReference<ChunkDestination>> pull_dests;
        std::vector<int> buffer_ids;
        for (auto& slice : slices_per_array) {
          auto device_size = xla::ValueOrThrow(
              arrs[i]->pjrt_buffers()[0]->GetOnDeviceSizeInBytes());
          auto [start, limit, step, total_size] = slice.compute(device_size);
          if (step != 1 || start + total_size != limit || limit > device_size) {
            throw nb::value_error(
                absl::StrFormat("Invalid slice (strides are not supported): %s "
                                "for buffer of size: %d",
                                nb::repr(slice).c_str(), device_size)
                    .c_str());
          }
          std::vector<xla::Future<>> futures_per_array;
          for (auto& buffer : arrs[i]->pjrt_buffers()) {
            auto raw_buffer = xla::ValueOrThrow(
                xla::PjRtRawBuffer::CreateRawAliasOfBuffer(buffer.get()));
            tsl::RCReference<ChunkDestination> dest;
            xla::Future<> future;
            std::tie(dest, future) = xla::ValueOrThrow(
                CreateSlicedRawBufferDest(raw_buffer, start, total_size));
            futures_per_array.push_back(std::move(future));
            pull_dests.push_back(std::move(dest));
            buffer_ids.push_back(static_cast<int>(buffer_ids.size()));
          }
          futures.emplace_back(xla::JoinFutures(futures_per_array));
          ++i;
        }

        self.Pull(uuid_cpp, buffer_ids, std::move(pull_dests));

        return futures;
      });

  nb::class_<PyTransferServer>(m, "TransferServer")
      .def("address", [](PyTransferServer& self) { return self.address(); })
      .def("_await_pull_flat",
           [](PyTransferServer& self, nb::int_ uuid,
              std::vector<jax::PyArray> inputs) {
             std::vector<xla::ifrt::ArrayRef> arrs;
             arrs.reserve(inputs.size());
             for (const jax::PyArray& input : inputs) {
               arrs.push_back(tsl::FormRef(input.ifrt_array()));
             }
             uint64_t uuid_cpp;
             try {
               uuid_cpp = static_cast<uint64_t>(uuid);
             } catch (std::out_of_range& e) {
               throw nb::value_error(
                   "_await_pull_flat requires uuid to fit in a uint64_t");
             }
             self.AwaitPull(uuid_cpp, arrs);
           })
      .def("_reset_rendevous_table",
           [](PyTransferServer& self) { self.Reset(); })
      .def("connect", [](PyTransferServer& self, const std::string& address) {
        return self.Connect(address);
      });
  m.def("_make_error_array", [](jax::nb_class_ptr<jax::PyClient> py_client,
                                nb::object py_aval, std::string message) {
    auto* ifrt_client =
        llvm::dyn_cast_or_null<xla::ifrt::PjRtClient>(py_client->ifrt_client());
    if (ifrt_client == nullptr) {
      xla::ThrowIfError(absl::InvalidArgumentError(
          "_pull_flat only supported on pjrt-ifrt clients."));
    }
    jax::PyUserContextScope user_context_scope;
    auto aval = xla::ValueOrThrow(ArraySpecFromShapeDtypeStruct(py_aval));
    xla::ifrt::PjRtArray::PjRtBuffers buffers;
    auto prim_type = xla::ValueOrThrow(xla::ifrt::ToPrimitiveType(aval.dtype));
    auto shards = xla::ValueOrThrow(aval.sharding->Disassemble(
        aval.shape, xla::ifrt::SingleDeviceShardSemantics::kAddressableShards));
    buffers.reserve(shards.size());
    for (auto& shard : shards) {
      auto* mem_space =
          xla::ValueOrThrow(MemorySpaceFromSharding(*shard.second));
      xla::PjRtClient::ShapeSpec shape_spec = {
          prim_type, xla::DimensionVector(shard.first.dims().begin(),
                                          shard.first.dims().end())};
      auto atm = xla::ValueOrThrow(
          py_client->pjrt_client()->CreateBuffersForAsyncHostToDevice(
              {shape_spec}, std::nullopt, mem_space));

      atm->SetBufferError(0, absl::InternalError(message));
      buffers.push_back(atm->RetrieveBuffer(0));
    }
    auto arr = xla::ValueOrThrow(xla::ifrt::PjRtArray::Create(
        ifrt_client, aval.dtype, aval.shape, aval.sharding, std::move(buffers),
        aval.layout));
    return jax::PyArray::MakeFromIfrtArrayAndSharding(
        py_client, std::move(arr), py_aval.attr("sharding"), false, true,
        /*skip_checks=*/false);
  });

  m.def(
      "start_transfer_server",
      [](jax::nb_class_ptr<jax::PyClient> py_client, std::string address,
         std::vector<std::string> transport_addresses_str,
         size_t max_num_parallel_copies, size_t transfer_size,
         bool supports_pinned_allocator,
         bool use_raw_buffers) -> PyTransferServer {
        PyTransferServer result;
        std::vector<SocketAddress> transport_addresses;
        transport_addresses.reserve(transport_addresses_str.size());
        for (const std::string& addr : transport_addresses_str) {
          transport_addresses.push_back(
              xla::ValueOrThrow(SocketAddress::Parse(addr)));
        }
        xla::ThrowIfError(result.Start(
            py_client->ifrt_client(), max_num_parallel_copies, transfer_size,
            xla::ValueOrThrow(SocketAddress::Parse(address)),
            transport_addresses, supports_pinned_allocator, use_raw_buffers));
        return result;
      },
      nb::arg("client"), nb::arg("address") = SocketAddress().ToString(),
      nb::arg("transport_addresses") = std::vector<std::string>(),
      nb::arg("max_num_parallel_copies") = 8,
      nb::arg("transfer_size") = 256 * 1024 * 1024,
      // Dual pinning not confirmed to be supported.
      nb::arg("supports_pinned_allocator") = false,
      // Technically unsafe (because a future donation won't wait for the
      // transfer to complete).
      nb::arg("use_raw_buffers") = false);
  m.def(
      "make_transfer_server_interface_factory",
      [](size_t transfer_size, int cross_host_transfer_timeout_seconds,
         std::shared_ptr<xla::DistributedRuntimeClient> distributed_client,
         const std::string& socket_address,
         const std::vector<std::string>& transport_addresses)
          -> xla::ifrt::TransferServerInterfaceFactory {
        std::shared_ptr<xla::KeyValueStoreInterface> kv_store =
            xla::GetDistributedKeyValueStore(distributed_client,
                                             "transfer_server:");
        auto factory_fn = xla::ValueOrThrow(
            xla::ifrt::PjRtTransferServer::MakePjRtTransferServerFactory(
                transfer_size,
                absl::Seconds(cross_host_transfer_timeout_seconds), kv_store,
                socket_address, transport_addresses));
        return xla::ifrt::TransferServerInterfaceFactory{std::move(factory_fn)};
      },
      nb::arg("transfer_size") = 256 * 1024 * 1024,
      nb::arg("cross_host_transfer_timeout_seconds") = 60,
      nb::arg("distributed_client").none() = nullptr,
      nb::arg("socket_address") = SocketAddress().ToString(),
      nb::arg("transport_addresses") = std::vector<std::string>());
}

}  // namespace aux
